| name | explain-diff-html |
|---|---|
| description | Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output. |
Please make me a rich, interactive explanation of the specified code change.
It should have these sections:
Discover gists
jscott3201
/ custom_pub_chat_template_qwen36.jinja
Created
May 25, 2026 17:08
A drop-in replacement chat template for Qwen/Qwen3.6-27B tuned for open-source agentic coding harnesses.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| {#--------------------------------------------------------------------- | |
| custom_pub_chat_template_qwen36.jinja | |
| ===================================== | |
| A public, harness-friendly fork of Qwen's Qwen3.6-27B chat template, | |
| tuned for open-source agentic coding harnesses like: | |
| - anomalyco/opencode (https://github.com/anomalyco/opencode) | |
| - earendil-works/pi (https://github.com/earendil-works/pi) | |
| - openclaw, OpenHarness, similar Claude-Code-style harnesses | |
| WHY THIS FORK EXISTS |
AfterRealm
/ BUILD-YOUR-OWN.md
Last active
July 6, 2026 05:56
Build Your Own Brain for Claude — a practical guide to persistent memory with Claude Code
A practical guide to building a persistent memory system for Claude Code. Based on the Vox Memori architecture — a brain-inspired system that gives Claude genuine continuity across sessions.
What you'll end up with: Claude that remembers you, learns your patterns, tracks your projects, and picks up exactly where you left off — every session.
What you need:
- Claude Code CLI (Max subscription recommended for off-peak usage)
- Python 3.10+
- Basic comfort with SQLite, Python, and JSON
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
NewerOlder